Various techniques exist for objectively measuring video quality. These techniques use different approaches for measuring the quality of the video that include one of a full-reference approach, a reduced-reference approach, and a no-reference approach. The full-reference approach to measuring video quality entails comparing characteristics, viz., spectral components, variations energy level variations, energy distributions in the frequency domain, and the like, of a target video with corresponding characteristics of a reference version of the target video, i.e., a reference video. The comparison is performed to detect a possible degradation in either of the above mentioned characteristics caused due to processing and/or transmission of the target video. The results of the comparison are used to obtain a measurement of the perceptual quality of the target video.
To accurately perform video quality assessment, a full-reference approach based system needs to be provided all the characteristics information corresponding to the reference video. However, transmitting characteristics information of the reference video over a network to an endpoint, such as a mobile device, consumes a considerable network bandwidth, rendering the full-reference approach based video quality assessment impractical.
The reduced-reference approach based video quality measurement entails comparison of a reduced number of characteristics corresponding to the target video with that of the reference video to provide a measurement of the perceptual quality of the target video. Thus, the reduced-reference approach requires transmission of reduced amount of characteristics information to the endpoint for performing video quality assessment, thereby reducing network bandwidth requirement. Further, the no-reference approach based video quality measurement does not entail comparison of characteristics information corresponding to the reference and target videos, thereby eliminating the need for transmitting characteristics information corresponding to the reference video over the network.
However, the above described video quality assessment systems are have several shortcomings. For example, the full-reference approach based measurement systems require transmission of substantial amount of characteristics information over the network, thereby leading to congestion during peak load scenarios. Although, the reduced-reference approach based measurement systems require less amount of characteristics information, yet transmitting any extra information leads to loss of precious network bandwidth. Further, the existing video quality measurement models that measure video quality using either of the full-reference, reduced-reference, and no-reference approaches conform only approximately to the human visual system (HVS). Thus, they are not able to provide a video quality estimate that accurately quantifies video quality assessment of a human eye, thereby leading to sub-par viewing experience at the endpoint.
In light of the above, there is a need for an invention that does not require transmission of the characteristics information corresponding to the reference video, that accurately conforms to the HVS, and that eliminates the shortcomings of the conventional video quality measurement models.